Phonology has both a taxonomic/descriptive and cognitive meaning. In the taxonomic/descriptive context, it refers to speech sound systems. As a cognitive term, it refers to a part of the brain’s ability to produce and perceive speech sounds. This article focuses on research in the cognitive domain. The brain does not simply record speech sounds and “play them back.” It abstracts over speech sounds, and transforms the abstractions in nontrivial ways. Phonological cognition is about what those abstractions are, and how they are transformed in perception and production. There are many theories about phonological cognition. Some theories see it as the result of domain-general mechanisms, such as analogy over a Lexicon. Other theories locate it in an encapsulated module that is genetically specified, and has innate propositional content. In production, this module takes as its input phonological material from a Lexicon, and refers to syntactic and morphological structure in producing an output, which involves nontrivial transformation. In some theories, the output is instructions for articulator movement, which result in speech sounds; in other theories, the output goes to the Phonetic module. In perception, a continuous acoustic signal is mapped onto a phonetic representation, which is then mapped onto underlying forms via the Phonological module, which are then matched to lexical entries. Exactly which empirical phenomena phonological cognition is responsible for depends on the theory. At one extreme, it accounts for all human speech sound patterns and realization. At the other extreme, it is little more than a way of abstracting over speech sounds. In the most popular Generative conception, it explains some sound patterns, with other modules (e.g., the Lexicon and Phonetic module) accounting for others. There are many types of patterns, with names such as “assimilation,” “deletion,” and “neutralization”—a great deal of phonological research focuses on determining which patterns there are, which aspects are universal and which are language-particular, and whether/how phonological cognition is responsible for them. Phonological computation connects with other cognitive structures. In the Generative T-model, the phonological module’s input includes morphs of Lexical items along with at least some morphological and syntactic structure; the output is sent to either a Phonetic module, or directly to the neuro-motor interface, resulting in articulator movement. However, other theories propose that these modules’ computation proceeds in parallel, and that there is bidirectional communication between them. The study of phonological cognition is a young science, so many fundamental questions remain to be answered. There are currently many different theories, and theoretical diversity over the past few decades has increased rather than consolidated. In addition, new research methods have been developed and older ones have been refined, providing novel sources of evidence. Consequently, phonological research is both lively and challenging, and is likely to remain that way for some time to come.
Paul de Lacy
A computational learner needs three things: Data to learn from, a class of representations to acquire, and a way to get from one to the other. Language acquisition is a very particular learning setting that can be defined in terms of the input (the child’s early linguistic experience) and the output (a grammar capable of generating a language very similar to the input). The input is infamously impoverished. As it relates to morphology, the vast majority of potential forms are never attested in the input, and those that are attested follow an extremely skewed frequency distribution. Learners nevertheless manage to acquire most details of their native morphologies after only a few years of input. That said, acquisition is not instantaneous nor is it error-free. Children do make mistakes, and they do so in predictable ways which provide insights into their grammars and learning processes. The most elucidating computational model of morphology learning from the perspective of a linguist is one that learns morphology like a child does, that is, on child-like input and along a child-like developmental path. This article focuses on clarifying those aspects of morphology acquisition that should go into such an elucidating a computational model. Section 1 describes the input with a focus on child-directed speech corpora and input sparsity. Section 2 discusses representations with focuses on productivity, developmental paths, and formal learnability. Section 3 surveys the range of learning tasks that guide research in computational linguistics and NLP with special focus on how they relate to the acquisition setting. The conclusion in Section 4 presents a summary of morphology acquisition as a learning problem with Table 4 highlighting the key takeaways of this article.
Jane Chandlee and Jeffrey Heinz
Computational phonology studies the nature of the computations necessary and sufficient for characterizing phonological knowledge. As a field it is informed by the theories of computation and phonology. The computational nature of phonological knowledge is important because at a fundamental level it is about the psychological nature of memory as it pertains to phonological knowledge. Different types of phonological knowledge can be characterized as computational problems, and the solutions to these problems reveal their computational nature. In contrast to syntactic knowledge, there is clear evidence that phonological knowledge is computationally bounded to the so-called regular classes of sets and relations. These classes have multiple mathematical characterizations in terms of logic, automata, and algebra with significant implications for the nature of memory. In fact, there is evidence that phonological knowledge is bounded by particular subregular classes, with more restrictive logical, automata-theoretic, and algebraic characterizations, and thus by weaker models of memory.